In this tutorial we will discuss performing co-registration of the multiple imaging modalities to the T1-weighted image.

library(ms.lesion)
library(neurobase)

Warning: package 'neurobase' was built under R version 3.3.2

Loading required package: oro.nifti

Warning: package 'oro.nifti' was built under R version 3.3.2

oro.nifti 0.7.2

files = get_image_filenames_list_by_subject()$training01
t1_fname = files["MPRAGE"]
t1 = readnii(t1_fname)

The function within_visit_registration takes in:

• fixed image - the image to be registered to
• moving images - images to register to the fixed
• typeofTransform - transformation of moving to fixed image (Rigid/Affine)
• interpolator - how are voxels averaged in fixed space

and outputs a list of transformations and output filenames

res = within_visit_registration(
  fixed = files["MPRAGE"],
  moving = files[c("T2", "FLAIR", "PD")],
  typeofTransform = "Rigid", 
  interpolator = "Linear"
)
output_imgs = lapply(res, function(x) x$outfile)
names(output_imgs) = c("T2", "FLAIR", "PD")
out = c(MPRAGE = list(t1), output_imgs)

multi_overlay(out)

double_ortho(out$MPRAGE, out$T2 )

• Rigid-body registration (linear) - 6 degrees of freedom (dof)
  • Co-registration (within the same person)
    • Cross-sectional between-sequences
    • Longitudinal within-sequence
    • Longitudinal between-sequences
• Affine registration – 12 dof
• Non-linear (> 12 dof)
  • Usually require a prior affine registration
  • Across-subject registration
  • Registration to a template
    • There are many different templates

• Rigid-body registration (linear) - 6 degrees of freedom (dof)
  • Co-registration (within the same person)
    • Cross-sectional between-sequences
    • Longitudinal within-sequence
    • Longitudinal between-sequences
• Affine registration – 12 dof
• Non-linear (> 12 dof)
  • Usually require a prior affine registration
  • Across-subject registration
  • Registration to a template
    • There are many different templates

For a voxel \(v\), the rigid transformation can be written as:

\[T_{\rm rigid}(v) = Rv + t\] where \(R =\) \[\left[\begin{array}{ccc} \cos\beta\cos\gamma& \cos\alpha\sin\gamma + \sin\alpha\sin\beta\cos\gamma & \sin\alpha\sin\gamma - \cos\alpha\sin\beta\cos\gamma \\ -\cos\beta\sin\gamma & \cos\alpha\cos\gamma - \sin\alpha\sin\beta\sin\gamma & \sin\alpha\cos\gamma + \cos\alpha\sin\beta\sin\gamma \\ \sin\beta & -\sin\alpha\cos\beta & \cos\alpha\cos\beta \end{array}\right]\]

• 6 degrees of freedom
• \(3\) associated with the translation vector: \(t=(t_x, t_y, t_z)\)
• \(3\) associated with the rotation parameters: \(\theta=(\alpha, \beta,\gamma)\).

• Pitch - Think of nodding ("yes")
• Yaw - Think of shaking head ("no") (SMH)
• Roll - Think of shoulder shrugging ("I don't know")
• x – left/right
• y – forward/backward
• z – jump up/down

First, we will register scans within a visit to the T1 of that visit:

• Requires fewer degrees of freedom
  • sequences from the same individual/brain are more alike than images from different subjects
• Example analyses that do not require a reference template
  • Identify location-specific longitudinal changes within an individual
  • Tissue class or structural segmentation
  • Analysis of indvidual-subject change in intensities

We will use the data from the Kirby21 dataset. Here we read in the T1 (denoted by MPRAGE) scan from visit 1:

• From FSL: ``FLIRT (FMRIB's Linear Image Registration Tool) is a fully automated robust and accurate tool for linear (affine) intra- and inter-modal brain image registration''

• Here we will register the scan with the skull on.

• Overall, there seems to be good overlap after registration with ANTsR
• Although we ran the registration on the raw data, it is usually beneficial to do inhomogeneity correction before registration.

The extrantsr function preprocess_mri_within will do the following steps:

1. Inhomogeneity correction
2. Registration of the files to the first filename

Now that the images are in the same space as the T1, if we skull-strip the T1 image, we can apply this mask to those images to extract brain tissues.

mask = readnii("../output/training01_01_mprage_mask.nii.gz")
sub_mask = applyEmptyImageDimensions(mask, inds = dd$inds)
masked_imgs = lapply(xout, mask_img, sub_mask)

orthographic(masked_imgs[[2]])

• Registration within a subject can be done in R
  • ants_regwrite wraps around the reading/writing of images and applying transformations
  • double_ortho and ortho2 can provide some basic visual checks to assess registration quality
  • preprocess_mri_within and preprocess_mri_across are general wrapper functions to process MRI data
• Once images are registered in the same space, operations can be applied to all the images, such as:
  • Masking with a brain mask
  • Transforming images to new spaces with one modality